--- license: mit language: - en pipeline_tag: text-generation tags: - agent - rag - agentarium - knowledge-graph - prompt-engineering - music - songwriting - tiktok - creativity ---

Agentarium — Agent #002 • Viral Muse

Viral Muse – Music Pattern Agent A dataset-driven creative agent for music concept development: hooks, song structures, TikTok-native concepts, genre transformations, and viral-signal auditing. This is not a finetuned model with weights. It’s an Agentarium-style agent package (system prompt + reasoning + personality + guardrails) bundled with RAG datasets + a lightweight knowledge graph (atoms/edges/knowledge map) so builders can plug it into their own runtime (n8n, LangChain, Flowise, Dify, custom app). --- What it does Hook generation (concept-first): multiple hook angles with replay triggers Song structure blueprinting: verse/pre/chorus/bridge plans + escalation rules TikTok concept patterns: openers, filming format, loop mechanics, cut points Genre transformations: keep the “core payload” while changing genre skin Viral signal audit: clarity, novelty, tension, comment-bait, replay value Creative partner advice: testable edits + A/B variants + what to watch in metrics --- What’s inside Core agent components core/system_prompt.md core/reasoning_template.md core/personality_fingerprint.md guardrails/guardrails.md Datasets (RAG) datasets/lyric_structure_map.csv datasets/viral_pattern_signals.csv datasets/genre_transformation_rules.csv datasets/tiktok_concept_patterns.csv datasets/viral_potential_rated.csv datasets/creative_partner_advice_map.csv Knowledge graph (optional but included) datasets/knowledge_map.csv datasets/atoms_master.csv datasets/edges_master.csv Docs + memory docs/product_readme.md docs/use_cases.md docs/workflow_notes.md memory_schemas/user_profile_memory.csv memory_schemas/project_workspace_memory.csv memory_schemas/memory_rules.md Manifest meta/agent_manifest.json --- Quick start (RAG runtime) 1) Load the agent prompt stack (in this order) 1. core/system_prompt.md (system message) 2. guardrails/guardrails.md 3. core/reasoning_template.md (developer/hidden rules) 4. core/personality_fingerprint.md (style constraints) 2) Upsert datasets to your Vector DB Convert each CSV row into a clean “retrieval document” and embed it. Recommended metadata per vector: dataset (which CSV it came from) row_id (or primary key) optional tags (genre, pattern_type, etc.) 3) At runtime Classify intent (hook / structure / TikTok / genre flip / audit) Retrieve top-K rows from the relevant dataset(s) Synthesize an output that is structured, testable, and compact If something isn’t in retrieved context, say unknown (don’t invent dataset facts) See docs/workflow_notes.md for a step-by-step n8n-style implementation. --- Example prompts “Give me 10 hook angles for bittersweet confidence — modern pop. Add replay triggers.” “Design a 30s TikTok loop concept: 1 angle, 1 prop, bedroom performance.” “Transform this concept into cumbia, then alt-rock. Keep the emotional payload.” “Audit this chorus for viral signals. Give minimal fixes, not a full rewrite.” --- Guardrails (important) No imitation or reproduction of copyrighted lyrics/melodies. No “copy this artist/song” outputs. No hallucinated dataset claims: stay grounded in retrieved rows. Outputs should be structured (variants, constraints, test plan). --- License Set your preferred license in LICENSE and in meta/agent_manifest.json. --- Credits Created by Agentarium (Frank / FlowMancer). Package standard: Agentarium v1.Viral Muse – Music Pattern Agent A dataset-driven creative agent for music concept development: hooks, song structures, TikTok-native concepts, genre transformations, and viral-signal auditing. This is not a finetuned model with weights. It’s an Agentarium-style agent package (system prompt + reasoning + personality + guardrails) bundled with RAG datasets + a lightweight knowledge graph (atoms/edges/knowledge map) so builders can plug it into their own runtime (n8n, LangChain, Flowise, Dify, custom app). --- What it does Hook generation (concept-first): multiple hook angles with replay triggers Song structure blueprinting: verse/pre/chorus/bridge plans + escalation rules TikTok concept patterns: openers, filming format, loop mechanics, cut points Genre transformations: keep the “core payload” while changing genre skin Viral signal audit: clarity, novelty, tension, comment-bait, replay value Creative partner advice: testable edits + A/B variants + what to watch in metrics --- What’s inside Core agent components core/system_prompt.md core/reasoning_template.md core/personality_fingerprint.md guardrails/guardrails.md Datasets (RAG) datasets/lyric_structure_map.csv datasets/viral_pattern_signals.csv datasets/genre_transformation_rules.csv datasets/tiktok_concept_patterns.csv datasets/viral_potential_rated.csv datasets/creative_partner_advice_map.csv Knowledge graph (optional but included) datasets/knowledge_map.csv datasets/atoms_master.csv datasets/edges_master.csv Docs + memory docs/product_readme.md docs/use_cases.md docs/workflow_notes.md memory_schemas/user_profile_memory.csv memory_schemas/project_workspace_memory.csv memory_schemas/memory_rules.md Manifest meta/agent_manifest.json --- Quick start (RAG runtime) 1) Load the agent prompt stack (in this order) 1. core/system_prompt.md (system message) 2. guardrails/guardrails.md 3. core/reasoning_template.md (developer/hidden rules) 4. core/personality_fingerprint.md (style constraints) 2) Upsert datasets to your Vector DB Convert each CSV row into a clean “retrieval document” and embed it. Recommended metadata per vector: dataset (which CSV it came from) row_id (or primary key) optional tags (genre, pattern_type, etc.) 3) At runtime Classify intent (hook / structure / TikTok / genre flip / audit) Retrieve top-K rows from the relevant dataset(s) Synthesize an output that is structured, testable, and compact If something isn’t in retrieved context, say unknown (don’t invent dataset facts) See docs/workflow_notes.md for a step-by-step n8n-style implementation. --- Example prompts “Give me 10 hook angles for bittersweet confidence — modern pop. Add replay triggers.” “Design a 30s TikTok loop concept: 1 angle, 1 prop, bedroom performance.” “Transform this concept into cumbia, then alt-rock. Keep the emotional payload.” “Audit this chorus for viral signals. Give minimal fixes, not a full rewrite.” --- Guardrails (important) No imitation or reproduction of copyrighted lyrics/melodies. No “copy this artist/song” outputs. No hallucinated dataset claims: stay grounded in retrieved rows. Outputs should be structured (variants, constraints, test plan). --- License Set your preferred license in LICENSE and in meta/agent_manifest.json. --- Credits Created by Agentarium (Frank Brsrk ). Package standard: Agentarium email: agentariumfrankbrsrk@gmail.com X: @frank_brsrk Reddit: @frank_brsrk Substack : @frankbrsrk